# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from __future__ import annotations

import unittest
from functools import partial
from typing import Any

import numpy as np
from program_config import ProgramConfig, TensorConfig
from trt_layer_auto_scan_test import TrtLayerAutoScanTest

import paddle.inference as paddle_infer


class TrtConvertSoftmaxTest(TrtLayerAutoScanTest):
    def is_program_valid(self, program_config: ProgramConfig) -> bool:
        inputs = program_config.inputs
        weights = program_config.weights
        outputs = program_config.outputs

        attrs = [
            program_config.ops[i].attrs for i in range(len(program_config.ops))
        ]

        # The input dimension should be less than or equal to the set axis.
        if len(inputs['softmax_input'].shape) <= attrs[0]['axis']:
            return False

        return True

    def sample_program_configs(self):
        def generate_input1(attrs: list[dict[str, Any]], batch):
            if self.dims == 4:
                return np.ones([batch, 3, 24, 24]).astype(np.float32)
            elif self.dims == 3:
                return np.ones([batch, 3, 24]).astype(np.float32)
            elif self.dims == 2:
                return np.ones([batch, 32]).astype(np.float32)
            elif self.dims == 1:
                return np.ones([batch]).astype(np.float32)
            elif self.dims == 0:
                return np.ones([]).astype(np.float32)

        for dims in [0, 1, 2, 3, 4]:
            for batch in [1, 2, 4]:
                for axis in [-1, 0, 1, 2, 3]:
                    self.dims = dims
                    dics = [{"axis": axis}, {}]
                    ops_config = [
                        {
                            "op_type": "softmax",
                            "op_inputs": {"X": ["softmax_input"]},
                            "op_outputs": {"Out": ["softmax_out"]},
                            "op_attrs": dics[0],
                        }
                    ]
                    ops = self.generate_op_config(ops_config)
                    program_config = ProgramConfig(
                        ops=ops,
                        weights={},
                        inputs={
                            "softmax_input": TensorConfig(
                                data_gen=partial(generate_input1, dics, batch)
                            )
                        },
                        outputs=["softmax_out"],
                    )

                    yield program_config

    def generate_dynamic_shape(self, attrs):
        if self.dims == 4:
            self.dynamic_shape.min_input_shape = {
                "softmax_input": [1, 3, 24, 24]
            }
            self.dynamic_shape.max_input_shape = {
                "softmax_input": [4, 3, 48, 48]
            }
            self.dynamic_shape.opt_input_shape = {
                "softmax_input": [1, 3, 24, 48]
            }
        elif self.dims == 3:
            self.dynamic_shape.min_input_shape = {"softmax_input": [1, 3, 24]}
            self.dynamic_shape.max_input_shape = {"softmax_input": [4, 3, 48]}
            self.dynamic_shape.opt_input_shape = {"softmax_input": [1, 3, 48]}
        elif self.dims == 2:
            self.dynamic_shape.min_input_shape = {"softmax_input": [1, 32]}
            self.dynamic_shape.max_input_shape = {"softmax_input": [4, 64]}
            self.dynamic_shape.opt_input_shape = {"softmax_input": [1, 32]}
        elif self.dims == 1:
            self.dynamic_shape.min_input_shape = {"softmax_input": [1]}
            self.dynamic_shape.max_input_shape = {"softmax_input": [4]}
            self.dynamic_shape.opt_input_shape = {"softmax_input": [1]}
        elif self.dims == 0:
            self.dynamic_shape.min_input_shape = {"softmax_input": []}
            self.dynamic_shape.max_input_shape = {"softmax_input": []}
            self.dynamic_shape.opt_input_shape = {"softmax_input": []}
        return self.dynamic_shape

    def sample_predictor_configs(
        self, program_config, run_pir=False
    ) -> tuple[paddle_infer.Config, list[int], float]:
        def clear_dynamic_shape():
            self.dynamic_shape.min_input_shape = {}
            self.dynamic_shape.max_input_shape = {}
            self.dynamic_shape.opt_input_shape = {}

        def generate_trt_nodes_num(attrs, dynamic_shape):
            if not dynamic_shape and (self.dims == 1 or self.dims == 0):
                return 0, 3
            return 1, 2

        attrs = [
            program_config.ops[i].attrs for i in range(len(program_config.ops))
        ]
        # for static_shape
        clear_dynamic_shape()
        if not run_pir:
            if attrs[0]['axis'] == 0:
                pass
            else:
                self.trt_param.precision = paddle_infer.PrecisionType.Float32
                program_config.set_input_type(np.float32)
                yield (
                    self.create_inference_config(),
                    generate_trt_nodes_num(attrs, False),
                    1e-5,
                )
                self.trt_param.precision = paddle_infer.PrecisionType.Half
                program_config.set_input_type(np.float16)
                yield (
                    self.create_inference_config(),
                    generate_trt_nodes_num(attrs, False),
                    1e-3,
                )

        # for dynamic_shape
        self.generate_dynamic_shape(attrs)
        self.trt_param.precision = paddle_infer.PrecisionType.Float32
        program_config.set_input_type(np.float32)
        yield (
            self.create_inference_config(),
            generate_trt_nodes_num(attrs, True),
            1e-5,
        )
        self.trt_param.precision = paddle_infer.PrecisionType.Half
        program_config.set_input_type(np.float16)
        yield (
            self.create_inference_config(),
            generate_trt_nodes_num(attrs, True),
            1e-3,
        )

    def test(self):
        self.run_test(run_pir=True)


if __name__ == "__main__":
    unittest.main()
